What is your favorite "data analysis" cartoon? - Cross Validated
385. Data analysis cartoons can be useful for many reasons: they help communicate; they show that quantitative people have a sense of humor too; they can instigate good teaching moments; and they can help us remember important principles and lessons. This is one of my favorites: As a service to those who value this kind of resource, please ...
Explain the xkcd jelly bean comic: What makes it funny?
2014年2月27日 · $\begingroup$ @Glen_b, the favorite data analysis cartoon thread is appropriately CW, however, I see no reason this one should be. 'Why funny' aside, the question asks for an understanding of the statistical point at issue in the cartoon, which has an answer & should be on-topic & not-CW (& which I think you handled well below). $\endgroup$
references - Statistics Jokes - Cross Validated
75. A mathematician, a physicist and a statistician went hunting for deer. When they chanced upon one buck lounging about, the mathematician fired first, missing the buck's nose by a few inches. The physicist then tried his hand, and missed the tail by a wee bit.
Examples for teaching: Correlation does not mean causation
83. There is an old saying: "Correlation does not mean causation". When I teach, I tend to use the following standard examples to illustrate this point: number of storks and birth rate in Denmark; number of priests in America and alcoholism; in the start of the 20th century it was noted that there was a strong correlation between 'Number of ...
data transformation - How to transform leptokurtic distribution to ...
Well, we can estimate the parameters of the distribution using MLE (or for methods of moments use IGMM()), ## mu_y = 2052; sigma_y = 491; skewness = 0; kurtosis = 13. and then use the bijective inverse transformation (based on W_delta()) to backtransform the data to the input X X, which -- by design -- should be very close to a normal.
How to calculate the signal-to-noise ratio (SNR) in an image?
This is an 'active' (or at least open) field of research. There are a few data driven methods to determine SNR on a single image, that exploit some assumptions on the spectral properties of the signal and the noise, and these are normally relatively broad and reasonable assumption. You may want to do a literature research on those. $\endgroup$
What's a real-world example of "overfitting"? - Cross Validated
2014年12月11日 · 20. The analysis that may have contributed to the Fukushima disaster is an example of overfitting. There is a well known relationship in Earth Science that describes the probability of earthquakes of a certain size, given the observed frequency of "lesser" earthquakes.
Imputation before or after splitting into train and test?
In this case, if you impute first with train+valid data set and split next, then you have used validation data set before you built your model, which is how a data leakage problem comes into picture. But you might ask, if I impute after splitting, it may be too tedious when I need to do cross validation. My suggest for that is to use sklearn ...
RNNs for Non-sequential Data - Cross Validated
2017年3月29日 · TLDR; RNN (especially Long-Term-Short-Term) allow you to specify a prior belief about relationships between events in "time", so you could benefit from it if those events are actually coupled (grouped) in some way even if they're not strictly sequential. Basically RNN provides a powerful way to apply linear/non-linear cross-"correlation" (much ...
Analyzing Ranked Data: Correlation and Factor Analysis?
2015年3月14日 · Whatever factor analysis or other multivariate analysis you do on the rankings data you should be aware that the ordered multinomial (no ties) nature of ranking task induces negative correlations in the the data. In your code, for example, you generate 6 variables which are random ranking from 1 to 6. Expected correlations between the variables ...